文献类型： 外文期刊
作者： Li, Wei 1 ; Zhu, Lizhou 2 ; Liu, Jun 3 ;
作者机构： 1.Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China
2.Southeast Univ, Coll Software Engn, Suzhou 215123, Peoples R China
3.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Peoples R China
关键词： leaf disease detection; PL-DINO; convolutional block attention module; equalization loss; crop
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）
ISSN：
年卷期： 2024 年 14 卷 5 期
页码：
收录情况： SCI
摘要： Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named "Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)". This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.
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